In this research, economic and financial development was the focus of this report, which examined how that growth affected oil prices. The country's economic and governmental plans are put into action. This study's objectives include developing policy alternatives for minimizing the economic and financial implications of oil price variations and making accurate future predictions of OPEC's oil drum prices. Oil price predictions can be improved even further by using the financial statement. Because the data contains two different types of time series (VECM and GARCH), Statistical software is the tool of choice for data analysis. In order to produce good predictions regarding oil prices, additional software (SPSS) is needed because the data is both linear and nonlinear. To predict the price of oil in the future, linear data can be analyzed using VECM (2,2,1).

In delegated management, it is common practice to manage portfolios within specific risk tolerances based on bench-mark indices. We provide insights into the fund manager’s optimal learning and investment behavior in response to changes in constituents of the benchmark. Our model allows us to examine the importance of allocating attention to newly listed stocks. The manager’s optimal effort exerted by the manager on new stock decreases as the constraint on tracking error becomes tighter. On the other hand, the optimal effort allocated to the new stock increases as the size of delegated funds increases. It is advisable to loosen the tracking error limits for delegated fund managers from the perspective of investor’s wealth under changes in benchmark constituents.

In the battlefield, tanks are tasked with suppressing enemy tanks and armored vehicles, and Armor-Piercing Fin-Stabilized Discarding Sabot (APFSDS) ammunition is suitable for such missions. While there have been claims suggesting that tanks are no longer necessary on the battlefield, countries worldwide continue to develop new tanks and advanced tank ammunition. This trend is evident in South Korea as well, emphasizing the need for policy recommendations regarding the development of tank ammunition. Therefore, in this study, we collected specification data on APFSDS from various countries developed to date, analyzing the evolutionary trends of tank ammunition through principal component analysis. To validate these results, classification models were employed. The significance of this research lies in confirming the development trend of tank ammunition from evolutionary perspective and providing policy recommendations essential for the tank-related initiatives of the South Korean Army.

In this research, we study the Iraqi economy, considering the exceptional circumstances it went through during the period of the study, represented by wars, economic sanctions, and finally the Coronavirus, which affected all the economies of the world, and the wrong policies generated many problems such as high prices, low exchange rate of the Iraqi dinar, and the deficit Budget, unemployment and others. Therefore, this research focuses on the primary issue facing the Iraqi economy: inflation and its correlation with the exchange rate. This is because the Central Bank, as the monetary authority, uses the exchange rate as a key tool to guide its monetary policies towards stability. We used the autoregressive vector model to analyze data spanning from 1990 to 2021. Where the direction of the causal relationship between the variables under study was determined and measured using the Cranger test for causality, as the exchange rate relationship with inflation and its time repercussions were described. We also used the co-integration test to ascertain the existence of a long-term relationship between the variables under study.

One of the major challenges in manufacturing systems is addressing bottlenecks. Managers and engineers strive to find methods to eliminate bottlenecks and reduce wait times in production lines. In this study, a simulation model of a manufacturing system was analyzed, focusing on bottlenecks and underutilized machinery. Validated simulation inputs were collected to build a model for optimizing the production line. The objective was to simulate the current production line to understand and analyze its bottlenecks and propose improvement solutions. The simulation model was developed using Arena software, revealing several bottleneck stations affecting production line efficiency. A model incorporating different parts of the production line and inventory management of cement was proposed to address this problem. Various improvement steps were analyzed using the simulation model to eliminate bottlenecks and enhance production line performance. As a result, the daily production capacity of the rotary kiln increased from 3699 to 8200 tons, more than doubling the current output.

Preprocessing and feature selection are important steps in the machine learning pipeline for text classification. These steps ensure that the input data are appropriately prepared for model training and that the most informative features are selected for the model to learn from. Morpheme analyzer is also crucial in machine learning performance, particularly in text classification tasks that involve languages with complex morphology. This study compares the performance of several feature selection methods, Korean morpheme analyzers, and classifiers in small-scale Korean text classification. PROMETHEE, a multi-criteria decision-making method (MCDM), is used to rank each method based on its performance. As per the results, the document frequency method exhibited the best overall performance among several feature selection methods. In terms of Korean morpheme analyzers, the most recently developed Khaiii demonstrated the best performance. The support vector machine classifier also exhibits suitable performance, suggesting that it is a reliable choice for small-scale Korean text classification. While performance differences among the methods were not significant for each dataset, the overall trend was clear, the aforementioned 3 methods consistently outperformed the others. Through this study, identification of effective techniques for small-scale Korean text classification is feasible, and it is expected to provide guidelines to analysists.

Objective: This study investigated public behavior regarding the acquisition of particulate matter (PM) information and how it is influenced by the perceived severity of the impact of PM on health in Korea and China. Background: Awareness of the harm caused by PM to health and the economy has recently increased. As PM pollution increases, people may seek related information through various channels to avoid the negative impact of PM. Method: An online survey was conducted in Korea and China. Results: The more severe the perceived impact of PM on health, the more frequently both Korean and Chinese persons sought PM information. For both groups, there were significant correlations between the perceived severity of health impact and diversity of means of information acquisition. Conclusion: In both China and Korea, awareness of PM may be increased via information provided by newspapers, neighbors and acquaintances, social media, government warning messages, and real-time monitoring applications. Application: The results of this study constitute valuable reference material for related academic and industrial research.

The management of physical assets has become a popular field of study over recent years and is being acknowledged in multiple disciplines worldwide. In this project, research on Physical Asset Management (PAM), maintenance and decision making are presented. PAM is a complex subject and requires the participation of multiple disciplines in order to successfully manage physical assets. Moreover, the management of maintenance makes a big contribution in achieving successful PAM. Decision making is a core element to manage maintenance efficiently. Various methods and techniques can be used to aid the decision making process such as, using decision-making methods. In this project, the importance of decision making is researched, especially in maintenance related decisions. The purpose of this research is to select the best maintenance strategy in conditions of uncertainty using the hybrid decision methods including AHP-GRAY, TOPSIS-GRAY, WASPAS-G, MULTIMOORA-G, COPRAS-G, ARAS-G and EDAS-G. It is done in three phases. The first phase uses the AHP-GRAY method and identifies a critical asset, for the second phase, three WASPAS-G, MULTIMOORA-G, and EDAS-G techniques that prioritize identifiable assets. In the third step, the most optimal Maintenance strategy for each of the failure modes was performed using three techniques TOPSIS-G, COPRAS-G, ARAS-G. An evaluation and validation of the toolbox is done through the case study to conclude whether it has value in practice or not.

Assessing the production process primarily revolves around quality. When dealing with a basic manufacturing process, quality can be easily anticipated. However, as manufacturing processes grow in complexity, it has been discovered through prior studies that directly predicting the quality of an intricate production system becomes challenging. This is due to the interdependency between each stage of manufacturing, where the outcomes of preceding stages impact subsequent processes. To address this issue, the Cascade Quality Prediction Method (CQPM) was developed. However, as this method employed a classification algorithm, CQPM cannot be directly applied when dealing with datasets that lack target variables for prediction or when the distribution of classes is imbalanced. This study aimed to improve the effectiveness of the CQPM in the context of multistage manufacturing. To achieve this, Hotelling’s T2 and the Synthetic Minority Over-sampling Technique (SMOTE) algorithm were incorporated during the data preparation phase, especially when dealing with imbalanced class distributions and missing target variables. The results demonstrated that the inclusion of Hotelling’s T2 allowed for the application of classification algorithms. By combining the CQPM approach with a random forest classifier and the SMOTE algorithm, notable improvements were observed in the model’s performance. The enhanced data pre-processing techniques led to impressive metric values, including 99.95% accuracy, 93.75% G-Mean, and 96.76% F-Measure. These findings indicate the potential of the proposed model framework to accurately predict quality in multistage manufacturing systems.

This paper aims to analyze and investigate the risk that facing the Iraqi economy based on mathematical models for the period 2003 to 2019. International uncertainties could dampen next market activity and create crises. The Kolmo-gorov-Smirnov Test, the normality test, and the Multicollinearity Test have all been run and shown to be consistent with the hypotheses. It was the need for communication between the Iraqi securities market and domestic financial and economic institutions that sparked this study&quots premise. Expected return and risk values of the Iraqi money market were analysed statistically using three different mathematical models. The study found various things, but the most important one was that the Iraqi securities market doesn&quott play much of a part in the country&quots economy. The risk of being hit hard by fallout from international crises that may be exacerbated by illegal capital flows leading to irrational speculation as a result of the anticipated openness. Vulnerability to external difficulties makes it tough to implement monetary and financial policies.